Providing high-quality healthcare services while being vigilant of the costs associated with such healthcare has traditionally been a goal of healthcare providers and related businesses. In a healthcare environment in which patients visit multiple doctors and healthcare providers across large geographic areas that may or may not communicate with one another, performing medical diagnostics or other tests to ascertain pertinent information about a patient can be inefficient due to the fact that tests and medical diagnoses may be duplicated amongst various healthcare providers that a patient has visited or may be performed without the benefit of information known and available from other healthcare providers.
Furthermore, the healthcare landscape is growing increasingly complex, which also can create tension with the goal of high-quality healthcare at low cost. The number of disease conditions known to clinicians is ever increasing as scientific discovery details a more granular understanding of pathogenesis, genetics, and sub-segmentation of previously less-understood conditions. The coding granularity by which such conditions are reflected within medical record documentation is similarly rising, as exemplified by the transition from ICD-9 (containing approximately 14,000 diagnosis codes) to ICD-10 (containing approximately 68,000 diagnosis codes) standards. The number and type of diagnostics available to clinicians is ever increasing, as is the number of treatment modalities.
In addition to the aforementioned rise in detailed granularity and complexity within the core practice of medicine, the business process, administration, and regulatory oversight surrounding healthcare is similarly rising in its complexity. Quality outcomes measurement metrics have risen dramatically in importance as the healthcare industry transitions froth volume-based to value-based care. The ability to assess the quality of care a patient has received in the past can be paramount to driving optimized healthcare decisions as well as driving healthcare costs down. However, often times the data required to make such an assessment can be stored in disparate places and entities that do not communicate with one another. Furthermore, the information required to make such assessments can be voluminous, thus making it untenable for a healthcare provider to make a “real-time” (during the period of a clinical encounter) assessment about the quality of care a patient has received and whether or not their care has complied with regulatory standards.
Computing platforms, in which data is aggregated, mined, and analyzed from multiple sources to put together a comprehensive and in-depth view of various facets of a patient's medical history “on-demand” (when needed and requested), can help to maximize the quality of healthcare while at the same-time minimizing inefficient expenditures associated with performing unnecessary or redundant medical tests and laboratory diagnostics. However, the aforementioned aggregation, mining, and analysis is often based upon large amounts of data that are spread over many different sources, and thus it is untenable for a healthcare provider to perform such analytics.